Results 11 to 20 of about 45,590 (292)
Image Segmentation and Feature Extraction Using Sift-Sad Algorithm for Disparity Map Generation
Communications, 2013 In this paper, a stereo matching algorithm based on image segments and disparity measurement using stereo images is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and Mean Shift algorithms
Patrik Kamencay +5 more
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Mapping of population disparities in the cholangiocarcinoma urinary metabolome [PDF]
Scientific Reports, 2021 AbstractPhenotypic diversity in urinary metabolomes of different geographical populations has been recognized recently. In this study, urinary metabolic signatures from Western (United Kingdom) and South-East Asian (Thai) cholangiocarcinoma patients were characterized to understand spectral variability due to host carcinogenic processes and/or ...
Munirah Alsaleh +22 more
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A reaction-diffusion model to capture disparity selectivity in primary visual cortex. [PDF]
PLoS ONE, 2011 Decades of experimental studies are available on disparity selective cells in visual cortex of macaque and cat. Recently, local disparity map for iso-orientation sites for near-vertical edge preference is reported in area 18 of cat visual cortex.
Mohammed Sultan Mohiuddin Siddiqui +1 more
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Dense disparity maps from sparse disparity measurements [PDF]
2011 International Conference on Computer Vision, 2011 In this work we propose a method for estimating disparity maps from very few measurements. Based on the theory of Compressive Sensing, our algorithm accurately reconstructs disparity maps only using about 5% of the entire map. We propose a conjugate subgradient method for the arising optimization problem that is applicable to large scale systems and ...
Simon Hawe +2 more
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Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps
IEEE Access, 2023 Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks such as depth/disparity estimation.
Maryam Hamad +3 more
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Stereo Matching by Filtering-Based Disparity Propagation. [PDF]
PLoS ONE, 2016 Stereo matching is essential and fundamental in computer vision tasks. In this paper, a novel stereo matching algorithm based on disparity propagation using edge-aware filtering is proposed.
Xingzheng Wang +3 more
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Map inference in the face of noise and disparity [PDF]
Proceedings of the 20th International Conference on Advances in Geographic Information Systems, 2012 This paper describes a process for automatically inferring maps from large collections of opportunistically collected GPS traces. In this type of dataset, there is often a great disparity in terms of coverage. For example, a freeway may be represented by thousands of trips, whereas a residential road may only have a handful of observations ...
James Biagioni, Jakob Eriksson
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Real‐time multi‐window stereo matching algorithm with fuzzy logic
IET Computer Vision, 2021 Stereo matching obtains a depth map called a disparity map that indicates or shows the positions of the objects in a scene. To estimate a disparity map, the most popular trend consists of comparing two images (left‐right) from two different points from ...
Héctor‐Daniel Vázquez‐Delgado +7 more
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RECOGNIZING TAMIL PALM-LEAF MANUSCRIPT CHARACTERS USING HYBRIDIZED HUMAN PERCEPTION BASED FEATURES
ICTACT Journal on Image and Video Processing, 2021 The paper presents a new Convolutional Neural Network (CNN) architecture, called stacked stereo CNN, for computing disparity map from stereo images. In stacked stereo CNN, left and right image patches are stacked back-to-back and fed to a single tower ...
Paramasivam Muthan Eswaran +2 more
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IMPROVING DISPARITY ESTIMATION BASED ON RESIDUAL COST VOLUME AND RECONSTRUCTION ERROR VOLUME [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Recently, great progress has been made in formulating dense disparity estimation as a pixel-wise learning task to be solved by deep convolutional neural networks.
J. Kang +4 more
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